Abstract
The paper is devoted to the parallel computing. The algorithm for roundwood volume estimation had insufficient performance so it was decided to port its bottleneck part on the GPU. The analysis of various GPGPU techniques was observed and the NVIDIA CUDA technology was chosen for implementation. The results of the research have shown the high potential of the GPU implementation in the improvement performance of the computation. The speedup of the algorithm for the roundwood volume estimation is more than 300% after porting on GPU with implementation of the CUDA technology. This helps to apply the machine vision algorithm in real-time system.
Highlights
Nowadays the GPGPU (General Purpose Computing on Graphic Processing Units) technology has come into widespread acceptance [1]
Hardware-software architecture CUDA (Compute Unified Device Architecture) developed by the NVIDIA is the implementation of the GPGPU technology
The result of the processing is a output image pixel referred to the center of the scanning area
Summary
Nowadays the GPGPU (General Purpose Computing on Graphic Processing Units) technology has come into widespread acceptance [1]. This programming technique is embedded in the data-flow computing concept. Data-flow computing is the paradigm of parallel computing that interpret data to be processed as a thread which elements could be processed independently parallel [2]. Hardware-software architecture CUDA (Compute Unified Device Architecture) developed by the NVIDIA is the implementation of the GPGPU technology. CUDA provides highlevel language (C and C++) programming to solve the complex computational problem in a less time due to the multi-core processing power of the GPU [3]
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